
import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase     :	Dict       			=  get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')




@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase_     (					_A    ,			unittest.TestCase	):


  '''simple docstring'''







  a__       			=						SpeechTaTokenizer
  a__       			=						False
  a__       			=						True
  def 				_lowercase				(					self     :					int       )     ->      int:







    """simple docstring"""






    super().setUp()

    # We have a SentencePiece fixture for testing
    __magic_name__								=			SpeechTaTokenizer(UpperCamelCase__       )

    __magic_name__								=			AddedToken("""<mask>"""     ,				lstrip=UpperCamelCase__     ,				rstrip=UpperCamelCase__       )
    __magic_name__								=			mask_token
    tokenizer.add_special_tokens({"""mask_token""": mask_token}       )
    tokenizer.add_tokens(["""<ctc_blank>"""]       )

    tokenizer.save_pretrained(self.tmpdirname       )
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					str       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			"""this is a test"""
    __magic_name__								=			"""this is a test"""
    return input_text, output_text
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					List[Any]=False     ,				UpperCamelCase__     :					Union[str, Any]=20     ,				UpperCamelCase__     :					Optional[Any]=5       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self.get_input_output_texts(UpperCamelCase__       )
    __magic_name__								=			tokenizer.encode(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__       )
    __magic_name__								=			tokenizer.decode(UpperCamelCase__     ,				clean_up_tokenization_spaces=UpperCamelCase__       )
    return text, ids
  def 				_lowercase				(					self     :					Dict       )     ->      int:







    """simple docstring"""






    __magic_name__								=			"""<pad>"""
    __magic_name__								=			1

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__       )     ,				UpperCamelCase__       )
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__       )     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			list(self.get_tokenizer().get_vocab().keys()       )

    self.assertEqual(vocab_keys[0]     ,				"""<s>"""       )
    self.assertEqual(vocab_keys[1]     ,				"""<pad>"""       )
    self.assertEqual(vocab_keys[-4]     ,				"""œ"""       )
    self.assertEqual(vocab_keys[-2]     ,				"""<mask>"""       )
    self.assertEqual(vocab_keys[-1]     ,				"""<ctc_blank>"""       )
    self.assertEqual(len(UpperCamelCase__       )     ,				81       )
  def 				_lowercase				(					self     :					int       )     ->      List[Any]:







    """simple docstring"""






    self.assertEqual(self.get_tokenizer().vocab_size     ,				79       )
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.get_tokenizers(do_lower_case=UpperCamelCase__       )
    for tokenizer in tokenizers:
      with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
        __magic_name__								=			tokenizer.vocab_size
        __magic_name__								=			len(UpperCamelCase__       )

        self.assertNotEqual(UpperCamelCase__     ,				0       )

        # We usually have added tokens from the start in tests because our vocab fixtures are
        # smaller than the original vocabs - let's not assert this
        # self.assertEqual(vocab_size, all_size)

        __magic_name__								=			["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
        __magic_name__								=			tokenizer.add_tokens(UpperCamelCase__       )
        __magic_name__								=			tokenizer.vocab_size
        __magic_name__								=			len(UpperCamelCase__       )

        self.assertNotEqual(UpperCamelCase__     ,				0       )
        self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
        self.assertEqual(UpperCamelCase__     ,				len(UpperCamelCase__       )       )
        self.assertEqual(UpperCamelCase__     ,				all_size + len(UpperCamelCase__       )       )

        __magic_name__								=			tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""     ,				add_special_tokens=UpperCamelCase__       )

        self.assertGreaterEqual(len(UpperCamelCase__       )     ,				4       )
        self.assertGreater(tokens[0]     ,				tokenizer.vocab_size - 1       )
        self.assertGreater(tokens[-3]     ,				tokenizer.vocab_size - 1       )

        __magic_name__								=			{"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
        __magic_name__								=			tokenizer.add_special_tokens(UpperCamelCase__       )
        __magic_name__								=			tokenizer.vocab_size
        __magic_name__								=			len(UpperCamelCase__       )

        self.assertNotEqual(UpperCamelCase__     ,				0       )
        self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
        self.assertEqual(UpperCamelCase__     ,				len(UpperCamelCase__       )       )
        self.assertEqual(UpperCamelCase__     ,				all_size_a + len(UpperCamelCase__       )       )

        __magic_name__								=			tokenizer.encode(
            """>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"""     ,				add_special_tokens=UpperCamelCase__       )

        self.assertGreaterEqual(len(UpperCamelCase__       )     ,				6       )
        self.assertGreater(tokens[0]     ,				tokenizer.vocab_size - 1       )
        self.assertGreater(tokens[0]     ,				tokens[1]       )
        self.assertGreater(tokens[-3]     ,				tokenizer.vocab_size - 1       )
        self.assertGreater(tokens[-3]     ,				tokens[-4]       )
        self.assertEqual(tokens[0]     ,				tokenizer.eos_token_id       )
        self.assertEqual(tokens[-3]     ,				tokenizer.pad_token_id       )
  def 				_lowercase				(					self     :					List[str]       )     ->      Optional[int]:







    """simple docstring"""






    pass
  def 				_lowercase				(					self     :					Dict       )     ->      str:







    """simple docstring"""






    pass
  def 				_lowercase				(					self     :					int       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.get_tokenizer()

    __magic_name__								=			tokenizer.tokenize("""This is a test"""       )
    # fmt: off
    self.assertListEqual(UpperCamelCase__     ,				[SPIECE_UNDERLINE, """T""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """a""", SPIECE_UNDERLINE, """t""", """e""", """s""", """t"""]       )
    # fmt: on

    self.assertListEqual(
        tokenizer.convert_tokens_to_ids(UpperCamelCase__       )     ,				[4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6]     ,				)

    __magic_name__								=			tokenizer.tokenize("""I was born in 92000, and this is falsé."""       )
    self.assertListEqual(
        UpperCamelCase__     ,				[SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """92000""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""]       )

    __magic_name__								=			tokenizer.convert_tokens_to_ids(UpperCamelCase__       )
    # fmt: off
    self.assertListEqual(UpperCamelCase__     ,				[4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26]       )
    # fmt: on

    __magic_name__								=			tokenizer.convert_ids_to_tokens(UpperCamelCase__       )
    self.assertListEqual(
        UpperCamelCase__     ,				[SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """<unk>""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""]       )





  @slow
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[
        """Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
        """general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
        """Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained """
        """models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
        """BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
        """conditioning on both left and right context in all layers.""",
        """The quick brown fox jumps over the lazy dog.""",
    ]

    # fmt: off
    __magic_name__								=			{
        """input_ids""": [
            [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
            [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
            [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
        ],
        """attention_mask""": [
            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        ]
    }
    # fmt: on

    self.tokenizer_integration_test_util(
        expected_encoding=UpperCamelCase__     ,				model_name="""microsoft/speecht5_asr"""     ,				revision="""c5ef64c71905caeccde0e4462ef3f9077224c524"""     ,				sequences=UpperCamelCase__     ,				)



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]=True       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			()

    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					str       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )
    __magic_name__								=			resnets

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					str     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					int=True       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			()

    for resnet in self.resnets:
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					Dict=True       )     ->      Union[str, Any]:







    """simple docstring"""






    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					str=True       )     ->      Dict:







    """simple docstring"""






    for resnet in self.resnets:
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[
        FlaxResnetBlockaD(
            in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
    ]

    __magic_name__								=			[]

    for _ in range(self.num_layers       ):
      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.in_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.in_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					int=True       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.resnets[0](UpperCamelCase__     ,				UpperCamelCase__       )
    for attn, resnet in zip(self.attentions     ,				self.resnets[1:]       ):
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    return hidden_states

